The hottest Neural Networks Substack posts right now

And their main takeaways
Category
Top Technology Topics

Hot Topics #20 (Mar. 1, 2023)

The Merge • 0 implied HN points • 01 Mar 23
🔬 Science Deep Learning Neural Networks
  1. Protein design using deep learning techniques to create custom biocatalysts
  2. Efficient de novo protein design through relaxed sequence space for better computational efficiency
  3. Improving robotic learning with corrective augmentation through NeRF for better manipulation policies

An Introduction to LSTM with Attention Model

Data Science Daily • 0 implied HN points • 01 Mar 23
🕹 Technology Machine Learning Neural Networks Artificial Intelligence Data science Applications
  1. LSTM models are good for handling input sequences of varied length like in language modeling and translation.
  2. Attention models help LSTM models focus on important parts of a sequence, improving accuracy.
  3. Combining LSTM with attention models can lead to better predictions and performance in tasks like natural language processing and image captioning.

🤓LSTM Networks: The Power of Long-Short-Term Memory and comparisons to ARIMA/XGBoost/Prophet

Data Science Daily • 0 implied HN points • 23 Feb 23
🕹 Technology Data science Neural Networks Machine Learning
  1. LSTM Networks can remember information for long periods and are great for processing sequential data.
  2. LSTMs can handle a wide variety of input and output types, making them flexible for real-world data.
  3. LSTMs are powerful for time series forecasting but can be computationally expensive, especially with large datasets.

AI Explainability Is About to Get Worse

The Grey Matter • 0 implied HN points • 21 Apr 23
🕹 Technology AI Neural Networks Language Models Training Data
  1. AI explainability for large language models like GPT models is becoming more challenging as these models advance.
  2. Examining the model, training data, and asking the model are the three main ways to understand these models' capabilities, each with its limitations.
  3. As AI capabilities advance, the urgency to develop better AI explainability techniques grows to keep pace with the evolving landscape.

OneClick Stable Diffusion Installer that comes prepackaged with SD1.5 and SD2.0

Barn Lab • 0 implied HN points • 25 Apr 23
🕹 Technology AI Software Installation Generative Art Neural Networks
  1. The OneClick Stable Diffusion Installer includes SD 1.5 and SD 2.0 models to simplify installation for users.
  2. The installer provides integrated model downloader to access famous models within the SD interface.
  3. For those interested in AI generative art, AUTOMATIC1111 is a feature-packed interface worth exploring after trying InvokeAI.
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Sketch of a new English orthography

Perambulations • 0 implied HN points • 07 May 23
🕹 Technology Language Neural Networks Machine Learning Writing
  1. English spelling is complex due to its accumulation of bits and pieces of other languages.
  2. Efforts for English spelling reform have included developing custom scripts and simplified spelling movements.
  3. An ideal English writing system may balance phonetic fidelity with concision, embed emphasis information, address vowel complexity, and include characters for high-frequency sound combinations.

Two-tower models for ranking problems

Simplicity is SOTA • 0 implied HN points • 22 May 23
🕹 Technology Big Data Machine Learning Research Neural Networks Algorithm
  1. Two-tower models are a technique being used in academia to improve ranking systems by looking into how position and user behavior affects clicks.
  2. Critiques have been raised against the two-tower models, questioning if they effectively separate biases and relevance in ranking.
  3. A new method called GradRev is emerging as a potential improvement over the previous two-tower models, applying a different approach to address bias in learning-to-rank systems.

DeOldify.NET

Barn Lab • 0 implied HN points • 07 Jun 23
🕹 Technology Neural Networks Image Processing
  1. Colorization of black-and-white images involves using color spaces like Lab to represent colors digitally
  2. Neural networks have been trained on colorized image datasets to aid in the colorization process
  3. DeOldify.NET offers a user-friendly way to colorize old images using AI without needing complex tools or specialized websites

What do we mean by inductive bias and expressiveness?

Simplicity is SOTA • 0 implied HN points • 19 Jun 23
🕹 Technology Machine Learning Neural Networks Models
  1. Inductive bias in machine learning refers to how models make choices in their learning process.
  2. Restriction bias limits the types of hypotheses considered in a model, while preference bias favors certain hypotheses over others.
  3. Expressiveness of a model determines the types of relationships it can capture, and can be enhanced by adding relevant features or interactions.

Primer on Machine Learning Interviews for Software Engineers

ExpandAI Newsletter • 0 implied HN points • 30 Jun 23
🕹 Technology Machine Learning Software Engineering Mathematics Neural Networks
  1. Software engineers in the future will likely require strong machine learning backgrounds.
  2. Machine learning interviews for software engineers cover software engineering, mathematics, and machine learning topics.
  3. Preparing for machine learning interviews should focus on optimizing for both software and machine learning skills.

DiffGrad : Is it the right optimization method for training your CNNs?

Machine Learning Diaries • 0 implied HN points • 25 Sep 23
🔬 Science Optimization Machine Learning Neural Networks Research
  1. Optimizing neural networks with DiffGrad may prevent slow learning and jittering effects in training
  2. DiffGrad adjusts learning rates based on gradient behavior for each parameter, leading to improved optimization
  3. Comparisons suggest that DiffGrad outperformed Adam optimizer in terms of avoiding overshooting global minima

How Far Are We From Being Able to Generate Whatever 3D Objects On the Fly?

I'll Keep This Short • 0 implied HN points • 17 Jul 23
🕹 Technology AI 3D Modeling Generative models Neural Networks Large Language Models
  1. AI-generated 3D objects are still far from being created instantly in real 3D
  2. Shap-E improves upon previous models by generating 3D objects using Neural Radiance Fields
  3. Although new technologies show promise, limitations like resource-intensive processes and lack of fine details still exist

Book Review: Why Machines Will Never Rule the World

The Grey Matter • 0 implied HN points • 17 Jul 23
🕹 Technology AI Machine Learning Artificial Intelligence Complex Systems Neural Networks
  1. The book emphasizes that machines will never rule the world, as AGI is fundamentally impossible due to computational limitations.
  2. The definitions of intelligence and machine intelligence play a crucial role in the argument against AGI.
  3. Language, context-dependence, and complex systems are central themes analyzed in the book to challenge the possibility of AGI.

Where Bayes Falls Short

As Clay Awakens • 0 implied HN points • 30 May 23
🔬 Science Statistics Machine Learning Neural Networks Deep Learning
  1. Deep learning algorithms are powerful for intelligence and learning, especially in contexts where Bayes' theorem falls short.
  2. Simpson's paradox shows how data separation can change conclusions based on initial beliefs.
  3. Deep learning approaches in regression tasks offer solutions without the need for ad-hoc choices, allowing for better predictions and generalization.

Beyond the Hype: Understanding the Science Behind AI and LLMs

The Novice • 0 implied HN points • 12 Nov 23
🕹 Technology AI Machine Learning Neural Networks Natural Language Processing Deep Learning
  1. Word2Vec created word associations in 3D space but didn't understand word meanings.
  2. Generative Pretrained Transformers (GPTs) improved upon Word2Vec by understanding word context and relationships.
  3. Chat GPT appears smart by storing and retrieving vast amounts of data quickly, but it's not truly intelligent.

Consciousness in one forward pass? An inconvenient thought experiment

Boris Again • 0 implied HN points • 07 Mar 24
🔬 Science Consciousness Neural Networks
  1. LLM, or large language models, like a calculator, perform sequential operations and don't have memories or reflections like humans do
  2. This thought experiment questions at what point a being loses consciousness when subjected to memory wipes and repetitive questions, similar to how LLM operates
  3. This experiment raises the question of when a rational being transitions to a machine-like 'calculator' state

Tiny Language Model

John Mayo-Smith's Substack • 0 implied HN points • 20 Apr 23
🕹 Technology AI Neural Networks Training Limitations Math
  1. The Tiny Language Model is a small functional language model that runs in your browser and learns based on a six-word customizable vocabulary, providing insights into more complex models like ChatGPT.
  2. The Tiny Language Model's training involves a compact 'corpus' from the vocabulary, showcasing a scaled-down version of the training process compared to models like ChatGPT, enhancing understanding through patterns in text.
  3. Observing the changes in weights (parameters) of the Tiny Language Model visually displays how the model is learning and can help identify areas for improvement in its training and performance.

Rust for Data Engineering

ingest this! • 0 implied HN points • 12 Mar 24
🕹 Technology Data Engineering Neural Networks Open Source Machine Learning
  1. Rust is reshaping data engineering by offering performance, safety, and concurrency, making it a strong contender alongside languages like Python.
  2. Learning Rust through 'The Rust Programming Language' book provides a solid foundation, with hands-on projects to enhance understanding.
  3. Mathesar is an open-source tool providing a spreadsheet-like interface to PostgreSQL databases, making data collaboration easier and more accessible.

How AI systems work, actually

Meaningness • 0 implied HN points • 06 Mar 23
🕹 Technology AI Algorithms Neural Networks
  1. Understanding AI systems requires more than just knowing they are neural networks trained with machine learning. It's important to grasp the specifics of how they work to understand their limitations and capabilities.
  2. Task-relevant, algorithmic understanding of AI systems is vital. This means comprehending the 'how' behind their operations in real-world situations, similar to understanding conventional database systems.
  3. Analysis of AI systems, like text generators, can reveal insights into human language use and understanding. Studying the patterns they exploit can shed light on how we process language, rather than just AI mechanisms.

Gradient Dissent

Meaningness • 0 implied HN points • 01 Mar 23
🕹 Technology Artificial Intelligence Engineering Neural Networks
  1. Neural networks are criticized for being expensive, unreliable, and potentially harmful, yet continue to be widely used without adequate safeguards.
  2. In the software industry, inferior designs can dominate better alternatives, leading to long-term use of buggy, slow, and complicated programs.
  3. Replacing neural networks with better alternatives is not only possible but important and urgent for creating a safer technological future.

Any Angel: Realness Scars

Do Not Research • 0 implied HN points • 15 Oct 22
🎨 Art & Illustration Neural Networks Illustrations Text Simulation
  1. The video essay 'Realness Scars' was written and illustrated by neural networks, with the script by OpenAI's GPT-3 and images by Midjourney.
  2. The text explores a landscape where representation is overshadowed by 'realness scars,' reflecting on traces of simulation absorbed by infrastructures.
  3. The collaboration between AI models like GPT-3 and artists like Midjourney can lead to innovative and thought-provoking creative projects.

10 Lesser-Known but Incredibly Useful Deep Learning Algorithms You Need to Master in 2024

AI Disruption • 0 implied HN points • 04 May 24
🕹 Technology Neural Networks Artificial Intelligence Machine Learning
  1. Deep learning algorithms like Word2vec, Variational Autoencoder, and Generative Adversarial Network have revolutionized machine learning applications with profound theories and elegant concepts.
  2. Graph Convolutional Network (GCN) advancements have simplified graph networks, leading to the development of powerful models in machine learning, like PointNet and Neural Radiance Field (NeRF) for 3D vision and modeling light behavior.
  3. Research in the era of large models focuses on technical advancements, diverse applications, theoretical foundations, and social impacts of AI, emphasizing the need for understanding the strengths and implications of utilizing large-scale models across various domains.